Framework to Accelerate Substance Use Disorder Genetic Studies through Customizable, EHR-Based Precision Phenotyping

通过可定制、基于 EHR 的精确表型分析加速药物滥用遗传研究的框架

• 批准号: 10512420
• 负责人: Alvin Dean Jeffery
• 金额: $ 47.55万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10512420
• 关键词: Alcohols; Basic Science; Biological; Clinical Decision Support Systems; Clinical Research; Code; Complex; Computer software; Custom; Data; Data Sources; Development; Disease; Disease Management; Drug usage; Electronic Health Record; Foundations; Genetic; Genetic study; Goals; Health; Individual; Intervention; Knowledge; Label; Link; Methods; National Institute of Drug Abuse; Opioid; Outcome; Pharmaceutical Preparations; Phenotype; Prevention; Public Health; Research; Research Personnel; Services; Specific qualifier value; Substance Use Disorder; System; Text; Tobacco; Variant; Work; base; clinical care; cost; disorder prevention; graphical user interface; heuristics; innovation; large datasets; neurobiological mechanism; novel; polygenic risk score; portability; preventive intervention; prospective; substance use treatment

Project Summary/Abstract There is a critical need for accurate, efficient, and portable substance use disorder (SUD) identification methods that support large-scale genetic studies in uncovering biological mechanisms helpful in SUD prevention and management. The long-term goal of this work is to support genetic discovery efforts that result in beneficial interventions for prevention and treatment of SUD. The overall objective in this proposal is to develop and evaluate an SUD phenotyping method that allows investigators to fine-tune phenotypes for their specific projects. Given most large-scale SUD phenotyping for genetics studies have relied on administrative billing codes (that undercount true cases) and binary outcome labels (that induce arbitrary dichotomization), the rationale for this project is that a system which includes a variety of data sources and generates a probabilistic outcome along a continuum is needed. The SUD phenotyping framework will support the inclusion of heterogenous electronic health record data types (including administrative billing codes, medication information, and unstructured text data) and will be evaluated in multiple organizations. Pairing these SUD phenotypes with genetic data will enhance our understanding of SUD mechanisms among individuals. That foundational work could ultimately result in the development of polygenic risk scores and clinical decision support systems that we could implement prospectively in clinical care. The proposed research is significant because researchers have a pressing need for SUD phenotyping approaches that can be customized to their research focus and available data. This proposal’s innovation lies in the creation of a “self-service” approach to SUD phenotype development in which a research team can specify their own phenotype definitions. The software will have a graphical user interface that makes the highest-yield rules/heuristics the easiest to use and can therefore be used by investigators with basic scientific programming knowledge. Through the activities outlined above, this innovation will directly accelerate genetic studies of SUD while simultaneously developing a precision phenotyping framework that can be applied to other disease domains.

项目总结/摘要 目前迫切需要准确、高效和便携的物质使用障碍（SUD）识别 支持大规模遗传研究的方法，以揭示有助于SUD的生物学机制 预防和管理。这项工作的长期目标是支持基因发现工作， 在预防和治疗SUD的有益干预措施。本建议的总体目标是 开发和评估SUD表型分型方法，使研究人员能够微调表型， 具体项目。鉴于遗传学研究的大多数大规模SUD表型分析依赖于行政管理， 计费代码（少算真实案例）和二元结果标签（诱导任意二分法）， 该项目的基本原理是，一个包括各种数据源并生成 需要沿连续体的概率结果沿着。SUD表型框架将支持纳入 异构电子健康记录数据类型（包括管理计费代码、药物 信息和非结构化文本数据），并将在多个组织中进行评估。将这些SUD配对 表型与遗传数据将增强我们对个体之间SUD机制的理解。的 基础工作最终可能导致多基因风险评分和临床决策的发展 支持我们可以在临床护理中前瞻性地实施的系统。所提出的研究是有意义的 因为研究人员迫切需要SUD表型分析方法，可以根据他们的需求定制。 研究重点和现有数据。该提案的创新之处在于创造了一种“自助”方法， SUD表型开发，研究团队可以指定自己的表型定义。的 软件将有一个图形用户界面，使最高收益的规则/算法最容易使用 因此可以由具有基本科学编程知识的研究者使用。通过活动 如上所述，这项创新将直接加速SUD的遗传研究，同时开发 一个精确的表型分析框架，可以应用于其他疾病领域。